Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations793
Missing cells34
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.8 KiB
Average record size in memory139.2 B

Variable types

DateTime1
Numeric10

Alerts

PV output is highly overall correlated with UV index and 4 other fieldsHigh correlation
UV index is highly overall correlated with PV output and 5 other fieldsHigh correlation
cloud cover is highly overall correlated with UV index and 3 other fieldsHigh correlation
humidity is highly overall correlated with PV output and 5 other fieldsHigh correlation
precip is highly overall correlated with humidity and 1 other fieldsHigh correlation
pressure is highly overall correlated with precipHigh correlation
solar energy is highly overall correlated with PV output and 5 other fieldsHigh correlation
solar radiation is highly overall correlated with PV output and 5 other fieldsHigh correlation
temp max is highly overall correlated with PV output and 4 other fieldsHigh correlation
temp min is highly overall correlated with temp maxHigh correlation
solar radiation has 33 (4.2%) missing values Missing
Unnamed: 0 has unique values Unique
precip has 179 (22.6%) zeros Zeros

Reproduction

Analysis started2025-02-13 16:59:27.211368
Analysis finished2025-02-13 16:59:35.459063
Duration8.25 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Date

Unique 

Distinct793
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Minimum2019-12-01 00:00:00
Maximum2022-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-13T16:59:35.520345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:35.627272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

precip
Real number (ℝ)

High correlation  Zeros 

Distinct520
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6528462
Minimum0
Maximum36.276
Zeros179
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:35.730535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.024
median0.506
Q33.34
95-th percentile11.5956
Maximum36.276
Range36.276
Interquartile range (IQR)3.316

Descriptive statistics

Standard deviation4.6044996
Coefficient of variation (CV)1.7356829
Kurtosis10.237003
Mean2.6528462
Median Absolute Deviation (MAD)0.506
Skewness2.881119
Sum2103.707
Variance21.201417
MonotonicityNot monotonic
2025-02-13T16:59:35.834012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 179
 
22.6%
0.024 24
 
3.0%
0.12 9
 
1.1%
0.095 7
 
0.9%
0.051 5
 
0.6%
0.006 5
 
0.6%
0.119 4
 
0.5%
0.014 4
 
0.5%
0.177 3
 
0.4%
0.096 3
 
0.4%
Other values (510) 550
69.4%
ValueCountFrequency (%)
0 179
22.6%
0.006 5
 
0.6%
0.009 2
 
0.3%
0.013 1
 
0.1%
0.014 4
 
0.5%
0.015 2
 
0.3%
0.019 1
 
0.1%
0.02 1
 
0.1%
0.024 24
 
3.0%
0.031 3
 
0.4%
ValueCountFrequency (%)
36.276 1
0.1%
25.899 1
0.1%
25.628 1
0.1%
25.455 1
0.1%
25.233 1
0.1%
24.499 1
0.1%
23.823 1
0.1%
22.441 1
0.1%
21.979 1
0.1%
21.854 1
0.1%

cloud cover
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.108701
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:35.938116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.48
Q153.4
median68.2
Q382.2
95-th percentile94.1
Maximum100
Range100
Interquartile range (IQR)28.8

Descriptive statistics

Standard deviation20.495106
Coefficient of variation (CV)0.3100213
Kurtosis0.071050789
Mean66.108701
Median Absolute Deviation (MAD)14.5
Skewness-0.68624848
Sum52424.2
Variance420.04936
MonotonicityNot monotonic
2025-02-13T16:59:36.230772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.4 6
 
0.8%
77.8 5
 
0.6%
70.9 5
 
0.6%
90.8 5
 
0.6%
60.3 5
 
0.6%
53 5
 
0.6%
67.9 5
 
0.6%
77.9 5
 
0.6%
83.1 4
 
0.5%
55.9 4
 
0.5%
Other values (463) 744
93.8%
ValueCountFrequency (%)
0 1
0.1%
2.6 1
0.1%
6.3 1
0.1%
8.5 1
0.1%
9.2 1
0.1%
9.8 1
0.1%
10.2 1
0.1%
10.5 1
0.1%
10.8 1
0.1%
11.4 1
0.1%
ValueCountFrequency (%)
100 2
0.3%
99.9 1
0.1%
99.8 1
0.1%
99.4 1
0.1%
99 1
0.1%
98.9 1
0.1%
98.5 1
0.1%
98 2
0.3%
97.7 1
0.1%
97.6 1
0.1%

solar radiation
Real number (ℝ)

High correlation  Missing 

Distinct637
Distinct (%)83.8%
Missing33
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean108.40868
Minimum7.2
Maximum319.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:36.331688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.2
5-th percentile14.19
Q132.45
median88.4
Q3165.55
95-th percentile276.095
Maximum319.7
Range312.5
Interquartile range (IQR)133.1

Descriptive statistics

Standard deviation84.538525
Coefficient of variation (CV)0.77981322
Kurtosis-0.6112742
Mean108.40868
Median Absolute Deviation (MAD)60.25
Skewness0.70579171
Sum82390.6
Variance7146.7622
MonotonicityNot monotonic
2025-02-13T16:59:36.434883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 6
 
0.8%
19.4 4
 
0.5%
52 4
 
0.5%
13.6 3
 
0.4%
10.5 3
 
0.4%
291.3 3
 
0.4%
39.4 3
 
0.4%
18.5 3
 
0.4%
47.4 3
 
0.4%
24 3
 
0.4%
Other values (627) 725
91.4%
(Missing) 33
 
4.2%
ValueCountFrequency (%)
7.2 1
 
0.1%
8.7 2
0.3%
8.9 1
 
0.1%
9 1
 
0.1%
9.2 1
 
0.1%
9.3 1
 
0.1%
10.5 3
0.4%
10.6 2
0.3%
10.7 1
 
0.1%
10.9 1
 
0.1%
ValueCountFrequency (%)
319.7 1
0.1%
308.8 1
0.1%
308.5 1
0.1%
307.7 1
0.1%
306.7 1
0.1%
306.4 1
0.1%
304.4 1
0.1%
303.2 1
0.1%
302 1
0.1%
301.9 1
0.1%

humidity
Real number (ℝ)

High correlation 

Distinct318
Distinct (%)40.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.71942
Minimum47.7
Maximum98.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:36.538167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.7
5-th percentile65.6
Q178.5
median85.4
Q391
95-th percentile96.2
Maximum98.9
Range51.2
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation9.5265724
Coefficient of variation (CV)0.11379167
Kurtosis0.31196316
Mean83.71942
Median Absolute Deviation (MAD)6.2
Skewness-0.84329988
Sum66389.5
Variance90.755582
MonotonicityNot monotonic
2025-02-13T16:59:36.642350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.2 8
 
1.0%
87.5 7
 
0.9%
89 7
 
0.9%
78.6 7
 
0.9%
92.8 7
 
0.9%
85.4 7
 
0.9%
90 7
 
0.9%
84.6 6
 
0.8%
89.2 6
 
0.8%
87.8 6
 
0.8%
Other values (308) 725
91.4%
ValueCountFrequency (%)
47.7 1
0.1%
50.8 1
0.1%
51 1
0.1%
53.4 1
0.1%
54.3 1
0.1%
55.2 1
0.1%
56.8 1
0.1%
57.1 1
0.1%
57.8 1
0.1%
58.1 1
0.1%
ValueCountFrequency (%)
98.9 1
0.1%
98.8 1
0.1%
98.6 1
0.1%
98.5 1
0.1%
98.3 2
0.3%
98 1
0.1%
97.9 1
0.1%
97.8 1
0.1%
97.6 1
0.1%
97.5 1
0.1%

pressure
Real number (ℝ)

High correlation 

Distinct381
Distinct (%)48.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1015.6939
Minimum975.1
Maximum1048.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:36.741957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum975.1
5-th percentile995.6
Q11008.3
median1016.75
Q31023.7
95-th percentile1032.845
Maximum1048.4
Range73.3
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation11.456059
Coefficient of variation (CV)0.011279046
Kurtosis0.30709175
Mean1015.6939
Median Absolute Deviation (MAD)7.25
Skewness-0.43720912
Sum804429.6
Variance131.24128
MonotonicityNot monotonic
2025-02-13T16:59:36.847298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1020.7 8
 
1.0%
1016.1 8
 
1.0%
1021.4 7
 
0.9%
1018.1 7
 
0.9%
1021.2 6
 
0.8%
1010.8 6
 
0.8%
1021.5 6
 
0.8%
1014.7 6
 
0.8%
1020.1 6
 
0.8%
1018 6
 
0.8%
Other values (371) 726
91.6%
ValueCountFrequency (%)
975.1 1
0.1%
976.1 1
0.1%
978.1 1
0.1%
979.5 1
0.1%
980.8 1
0.1%
982.8 1
0.1%
984.9 1
0.1%
985.3 1
0.1%
986.1 1
0.1%
986.2 1
0.1%
ValueCountFrequency (%)
1048.4 1
0.1%
1045.4 1
0.1%
1042.5 1
0.1%
1041.2 1
0.1%
1041.1 1
0.1%
1041 1
0.1%
1040.9 1
0.1%
1039.3 1
0.1%
1038.7 1
0.1%
1038.6 2
0.3%

UV index
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0504414
Minimum0
Maximum10
Zeros7
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:36.938422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4710399
Coefficient of variation (CV)0.61006683
Kurtosis-1.147914
Mean4.0504414
Median Absolute Deviation (MAD)2
Skewness0.30487992
Sum3212
Variance6.1060383
MonotonicityNot monotonic
2025-02-13T16:59:37.019355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 153
19.3%
2 120
15.1%
3 103
13.0%
5 94
11.9%
7 77
9.7%
8 77
9.7%
6 75
9.5%
4 70
8.8%
9 15
 
1.9%
0 7
 
0.9%
ValueCountFrequency (%)
0 7
 
0.9%
1 153
19.3%
2 120
15.1%
3 103
13.0%
4 70
8.8%
5 94
11.9%
6 75
9.5%
7 77
9.7%
8 77
9.7%
9 15
 
1.9%
ValueCountFrequency (%)
10 2
 
0.3%
9 15
 
1.9%
8 77
9.7%
7 77
9.7%
6 75
9.5%
5 94
11.9%
4 70
8.8%
3 103
13.0%
2 120
15.1%
1 153
19.3%

temp min
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.019546
Minimum-3.9
Maximum18.3
Zeros2
Zeros (%)0.3%
Negative48
Negative (%)6.1%
Memory size6.3 KiB
2025-02-13T16:59:37.110849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.9
5-th percentile-0.2
Q13.4
median7.2
Q310.7
95-th percentile14.2
Maximum18.3
Range22.2
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation4.5988315
Coefficient of variation (CV)0.65514657
Kurtosis-0.76558144
Mean7.019546
Median Absolute Deviation (MAD)3.7
Skewness-0.0065833945
Sum5566.5
Variance21.149251
MonotonicityNot monotonic
2025-02-13T16:59:37.212385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7 12
 
1.5%
7.3 11
 
1.4%
7.7 11
 
1.4%
11.3 10
 
1.3%
6.4 10
 
1.3%
7.1 10
 
1.3%
8.4 9
 
1.1%
12.9 9
 
1.1%
11.5 9
 
1.1%
6.2 9
 
1.1%
Other values (179) 693
87.4%
ValueCountFrequency (%)
-3.9 1
0.1%
-3.7 1
0.1%
-3.6 1
0.1%
-3.5 1
0.1%
-3 1
0.1%
-2.9 1
0.1%
-2.8 1
0.1%
-2.6 1
0.1%
-2.5 2
0.3%
-2.3 1
0.1%
ValueCountFrequency (%)
18.3 1
0.1%
18 1
0.1%
17.3 1
0.1%
17.1 1
0.1%
16.9 1
0.1%
16.8 1
0.1%
16.6 2
0.3%
16.4 1
0.1%
16.3 1
0.1%
16.1 1
0.1%

temp max
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.164817
Minimum-0.1
Maximum29.7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size6.3 KiB
2025-02-13T16:59:37.310779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1
5-th percentile5.16
Q19.4
median12.2
Q317.3
95-th percentile22.04
Maximum29.7
Range29.8
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.3409544
Coefficient of variation (CV)0.40569909
Kurtosis-0.32184585
Mean13.164817
Median Absolute Deviation (MAD)3.9
Skewness0.29165734
Sum10439.7
Variance28.525793
MonotonicityNot monotonic
2025-02-13T16:59:37.411613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.5 14
 
1.8%
11.5 12
 
1.5%
10.5 11
 
1.4%
10.4 10
 
1.3%
10.1 10
 
1.3%
8.4 10
 
1.3%
11.3 10
 
1.3%
17.3 9
 
1.1%
9.6 9
 
1.1%
18.6 9
 
1.1%
Other values (203) 689
86.9%
ValueCountFrequency (%)
-0.1 1
 
0.1%
0.4 1
 
0.1%
0.5 1
 
0.1%
1.3 1
 
0.1%
2 2
0.3%
2.1 1
 
0.1%
2.3 2
0.3%
2.6 3
0.4%
2.7 1
 
0.1%
2.8 1
 
0.1%
ValueCountFrequency (%)
29.7 1
0.1%
28.2 2
0.3%
28 1
0.1%
27.9 1
0.1%
27.6 1
0.1%
27.4 1
0.1%
26.9 1
0.1%
26.6 1
0.1%
26.4 2
0.3%
26.2 1
0.1%

solar energy
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.262169
Minimum0.7
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:37.515019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.2
Q12.8
median7.4
Q314.1
95-th percentile23.7
Maximum27.7
Range27
Interquartile range (IQR)11.3

Descriptive statistics

Standard deviation7.2473998
Coefficient of variation (CV)0.78247329
Kurtosis-0.56727778
Mean9.262169
Median Absolute Deviation (MAD)5
Skewness0.72444154
Sum7344.9
Variance52.524804
MonotonicityNot monotonic
2025-02-13T16:59:37.620076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 16
 
2.0%
1.5 14
 
1.8%
1.1 13
 
1.6%
1.3 13
 
1.6%
2.5 12
 
1.5%
2.4 12
 
1.5%
1 11
 
1.4%
1.7 11
 
1.4%
1.2 11
 
1.4%
3 10
 
1.3%
Other values (219) 670
84.5%
ValueCountFrequency (%)
0.7 4
 
0.5%
0.8 4
 
0.5%
0.9 5
 
0.6%
1 11
1.4%
1.1 13
1.6%
1.2 11
1.4%
1.3 13
1.6%
1.4 8
1.0%
1.5 14
1.8%
1.6 9
1.1%
ValueCountFrequency (%)
27.7 1
 
0.1%
26.8 1
 
0.1%
26.5 4
0.5%
26.3 1
 
0.1%
26.2 1
 
0.1%
26.1 6
0.8%
25.8 1
 
0.1%
25.7 3
0.4%
25.6 3
0.4%
25.5 1
 
0.1%

PV output
Real number (ℝ)

High correlation 

Distinct754
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8048613
Minimum0.078
Maximum6.873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2025-02-13T16:59:37.717888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.3274
Q10.855
median2.462
Q34.566
95-th percentile6.3754
Maximum6.873
Range6.795
Interquartile range (IQR)3.711

Descriptive statistics

Standard deviation2.04496
Coefficient of variation (CV)0.72907705
Kurtosis-1.2193134
Mean2.8048613
Median Absolute Deviation (MAD)1.732
Skewness0.37544347
Sum2224.255
Variance4.1818614
MonotonicityNot monotonic
2025-02-13T16:59:37.819805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.329 4
 
0.5%
0.635 3
 
0.4%
0.643 2
 
0.3%
0.963 2
 
0.3%
4.31 2
 
0.3%
3.482 2
 
0.3%
0.509 2
 
0.3%
2.468 2
 
0.3%
0.882 2
 
0.3%
4.749 2
 
0.3%
Other values (744) 770
97.1%
ValueCountFrequency (%)
0.078 1
0.1%
0.106 1
0.1%
0.123 1
0.1%
0.127 1
0.1%
0.137 1
0.1%
0.141 1
0.1%
0.154 1
0.1%
0.157 1
0.1%
0.162 1
0.1%
0.172 1
0.1%
ValueCountFrequency (%)
6.873 1
0.1%
6.87 1
0.1%
6.856 1
0.1%
6.809 1
0.1%
6.801 1
0.1%
6.798 1
0.1%
6.793 1
0.1%
6.787 1
0.1%
6.77 1
0.1%
6.756 1
0.1%

Interactions

2025-02-13T16:59:34.381889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.484746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.271295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.028473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.809167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.542448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.304794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.205751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.908622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.638882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.457471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.567414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.350543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.106424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.888074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.622239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.380121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.277187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.984528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.714147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.535556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.644892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.421798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.181884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.956666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.696706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.450750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.345248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.057291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.782961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.610721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.722167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.499171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.255720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.032404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.772109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.527121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.415588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.130512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.855952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.685752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.800018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.571513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.331600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.103863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.846159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.778089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.483946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.205231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.929321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.764449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.882996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.654233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.410625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.181918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.927384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.853873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.559374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.283207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.011354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.833466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:27.960114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.726419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.504728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.253943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.001071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.922559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.629148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.353859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.088626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.905362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.034186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.798769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.575079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.320626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.073641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.987867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.694217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.421709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.157862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.983835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.113929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.874364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.655829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.393267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.149995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.059248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.763990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.492174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.235412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:35.058996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.188676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:28.945802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:29.730824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:30.464043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:31.225756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.128782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:32.832694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:33.564169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-13T16:59:34.302935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-13T16:59:37.893934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
PV outputUV indexcloud coverhumidityprecippressuresolar energysolar radiationtemp maxtemp min
PV output1.0000.888-0.469-0.688-0.4490.2130.9400.9410.7130.440
UV index0.8881.000-0.585-0.707-0.4040.1670.9610.9610.6330.321
cloud cover-0.469-0.5851.0000.6700.350-0.120-0.570-0.579-0.1750.164
humidity-0.688-0.7070.6701.0000.552-0.257-0.732-0.739-0.3130.006
precip-0.449-0.4040.3500.5521.000-0.684-0.429-0.439-0.2270.056
pressure0.2130.167-0.120-0.257-0.6841.0000.1780.1860.055-0.126
solar energy0.9400.961-0.570-0.732-0.4290.1781.0001.0000.6880.371
solar radiation0.9410.961-0.579-0.739-0.4390.1861.0001.0000.7070.384
temp max0.7130.633-0.175-0.313-0.2270.0550.6880.7071.0000.841
temp min0.4400.3210.1640.0060.056-0.1260.3710.3840.8411.000

Missing values

2025-02-13T16:59:35.165743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-13T16:59:35.299333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-13T16:59:35.410235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0precipcloud coversolar radiationhumiditypressureUV indextemp mintemp maxsolar energyPV output
02019-12-010.00072.027.987.31024.42.00.95.42.40.748
12019-12-020.12041.2NaN82.01032.62.0-1.15.83.91.122
22019-12-030.00059.842.582.61027.73.02.18.23.71.091
32019-12-040.09626.243.890.61020.73.01.06.93.71.060
42019-12-050.13485.226.591.51019.42.01.99.72.30.712
52019-12-063.34087.419.290.71008.31.08.311.21.70.492
62019-12-070.21986.728.589.51013.21.06.99.82.20.776
72019-12-083.48967.828.483.61001.22.05.710.82.50.670
82019-12-098.22244.132.576.61013.72.02.58.62.90.944
92019-12-106.66180.711.390.71013.01.02.411.51.00.078
Unnamed: 0precipcloud coversolar radiationhumiditypressureUV indextemp mintemp maxsolar energyPV output
7832022-01-220.00097.621.783.41038.71.02.65.01.81.002
7842022-01-230.000100.019.386.31034.91.02.24.11.50.944
7852022-01-240.000100.09.289.01032.50.03.54.30.70.795
7862022-01-250.00099.916.290.11034.41.02.53.61.50.687
7872022-01-260.00087.932.686.71034.02.00.87.73.01.321
7882022-01-270.60564.023.691.71030.81.02.010.42.10.612
7892022-01-280.19161.840.895.91035.62.00.69.13.50.401
7902022-01-291.22677.831.093.51031.91.03.510.22.60.700
7912022-01-300.12438.853.988.91031.43.00.48.04.71.313
7922022-01-310.77373.035.679.01027.62.05.57.33.11.491